GeoVault: Leveraging Human Spatial Memory for Secure Cryptographic Key Management

Human-centered cryptographic key management is constrained by a persistent tension between security and usability. While modern cryptographic primitives offer strong theoretical guarantees, practical failures often arise from the difficulty users face in generating, memorizing, and securely storing high-entropy secrets. Existing mnemonic approaches suffer from severe entropy collapse due to predictable human choice, while machine-generated mnemonics such as BIP–39 impose significant cognitive burden. This paper introduces GeoVault, a spatially anchored key derivation framework that leverages human spatial memory as a cryptographic input. GeoVault derives keys from user-selected geographic locations, encoded deterministically and hardened using memory-hard key derivation functions. We develop a formal entropy model that captures semantic and clustering biases in human location choice and distinguishes nominal from effective spatial entropy under attacker-prioritized dictionaries. Through information-theoretic analysis and CPU–GPU benchmarking, we show that spatially anchored secrets provide a substantially higher effective entropy floor than human-chosen passwords under realistic attacker models. When combined with Argon2id, spatial mnemonics benefit from a hardware-enforced asymmetry that strongly constrains attacker throughput as memory costs approach GPU VRAM limits. Our results indicate that modest multi-point spatial selection combined with memory-hard derivation can achieve attacker-adjusted work factors comparable to those of 12-word BIP–39 mnemonics, while single-point configurations provide meaningful offline resistance with reduced cognitive burden.